A recent publication on information-age.com looked at new research into machine learning technologies and the biggest barriers faced in the adoption of these technologies, specifically addressing how a lack of human skill is ironically limiting the growth of many opportunities for expansion within multiple industries. I will now highlight certain key extracts from this new study and present a solution to the problems faced, hopefully convincing you that, in terms of lead generation at least, we can overcome these problems and begin moving forward with the next generation of machine learning technologies, today.
In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML — it’s second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.
“Although most IT buyers understand the benefits of machine learning, with 33% of respondents saying they have already seen tangible ROI from its use, many are still unsure about how to implement and how it will impact their businesses,” said Stephen Line, VP EMEA at Cloudera. “In what is still the early stages for many businesses in actually implementing ML, it’s unsurprising to learn that the skills gap and investment are key factors in preventing many companies from using it to improve efficiency and drive growth. That said, with the benefits of ML quite clear, the race is now on for businesses to overcome their barriers to deliver a better experience for their customers.”
Similar to data science, ML is progressing in a distinctly different way from other job markets. Because ML rotates around gathering, collating and interpreting data, it traverses numerous disciplines; maths, statistics, and programming are all required. It’s difficult to write this in a job description let alone actually find it.
As you can imagine, ML is pretty complicated stuff, and it’s not something just any old computer engineer can grasp. ML requires cream of the crop computer scientists who can deal with large volumes of data at scale.
A natural intuition for maths is essential. This contrasts, with traditional software developers, who don’t need to be that great at maths thanks to the availability of maths libraries and other functions that relieve them from doing equations the hard way. With ML, a developer needs to grasp complicated maths such as linear algebra, calculus and gradient descent.
As we all know, today there’s a dearth of skills in all areas of STEM. Information Age recently reported how 94% of business leaders surveyed by OpsRamp are having a “somewhat difficult” time trying to find candidates with the right technology and business skills to meet digital transformation goals.
Competition for this shallow pool of candidates is fierce, and the arrival of new roles is outstripping supply.
ML, has created a new profession. Interestingly, people are finding their way into it through unconventional routes. According to a study from data scientist community Kaggle, the vast majority of employed machine learning specialists today gained their skills by way of self-learning (27%) or a Massive Open Online Course (MOOC) (32%). Only 20% of people got their start in data science at a university, while 18% majored in math/statistics.
Read original source here:
The results of this study suggest we are at a crucial point in the application of machine learning. If you are reading this and identify with the 51% - you are open to using machine learning to optimise your performance but face problems with finding the human skill required to apply it - then I present you a simpler solution. With Databowl, ML can be easily applied without the need for a dedicated ML specialist. A data scientist is not required as the applications are built straight into the system. In terms of practical application and specific problems ML can solve, you can use Databowl to improve insight into customers and behaviour patterns, identify fraudulent leads and identify promising leads which you can then put on a fast track. In short, applying ML is a simple way to optimise the performance of your entire campaign!
If you’d like to learn more about how you can begin using machine learning to optimise your performance, take a look at our dedicated department, Skunkworx and get started with a better solution right now.
Tactics and tips served straight to your inbox. Sign up to the Databowl newsletter and receive weekly custom content for unlocking growth.